Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations58645
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.8 MiB
Average record size in memory104.0 B

Variable types

Numeric8
Categorical4
Boolean1

Alerts

cb_person_cred_hist_length is highly overall correlated with person_ageHigh correlation
cb_person_default_on_file is highly overall correlated with loan_grade and 1 other fieldsHigh correlation
loan_amnt is highly overall correlated with loan_percent_incomeHigh correlation
loan_grade is highly overall correlated with cb_person_default_on_file and 1 other fieldsHigh correlation
loan_int_rate is highly overall correlated with cb_person_default_on_file and 1 other fieldsHigh correlation
loan_percent_income is highly overall correlated with loan_amntHigh correlation
person_age is highly overall correlated with cb_person_cred_hist_lengthHigh correlation
id is uniformly distributed Uniform
id has unique values Unique
person_emp_length has 7586 (12.9%) zeros Zeros

Reproduction

Analysis started2025-03-11 18:53:44.943802
Analysis finished2025-03-11 18:53:52.301090
Duration7.36 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

id
Real number (ℝ)

Uniform  Unique 

Distinct58645
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29322
Minimum0
Maximum58644
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size458.3 KiB
2025-03-11T14:53:52.388952image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2932.2
Q114661
median29322
Q343983
95-th percentile55711.8
Maximum58644
Range58644
Interquartile range (IQR)29322

Descriptive statistics

Standard deviation16929.498
Coefficient of variation (CV)0.57736504
Kurtosis-1.2
Mean29322
Median Absolute Deviation (MAD)14661
Skewness0
Sum1.7195887 × 109
Variance2.8660789 × 108
MonotonicityStrictly increasing
2025-03-11T14:53:52.504832image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
39089 1
 
< 0.1%
39091 1
 
< 0.1%
39092 1
 
< 0.1%
39093 1
 
< 0.1%
39094 1
 
< 0.1%
39095 1
 
< 0.1%
39096 1
 
< 0.1%
39097 1
 
< 0.1%
39098 1
 
< 0.1%
Other values (58635) 58635
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
58644 1
< 0.1%
58643 1
< 0.1%
58642 1
< 0.1%
58641 1
< 0.1%
58640 1
< 0.1%
58639 1
< 0.1%
58638 1
< 0.1%
58637 1
< 0.1%
58636 1
< 0.1%
58635 1
< 0.1%

person_age
Real number (ℝ)

High correlation 

Distinct53
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.550857
Minimum20
Maximum123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size458.3 KiB
2025-03-11T14:53:52.624416image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile22
Q123
median26
Q330
95-th percentile39
Maximum123
Range103
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.0332158
Coefficient of variation (CV)0.21898469
Kurtosis6.4083796
Mean27.550857
Median Absolute Deviation (MAD)3
Skewness1.9654967
Sum1615720
Variance36.399693
MonotonicityNot monotonic
2025-03-11T14:53:52.762033image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23 7726
13.2%
22 7051
12.0%
24 6395
10.9%
25 5067
 
8.6%
27 4450
 
7.6%
26 3874
 
6.6%
28 3707
 
6.3%
29 3270
 
5.6%
30 2333
 
4.0%
31 1917
 
3.3%
Other values (43) 12855
21.9%
ValueCountFrequency (%)
20 12
 
< 0.1%
21 1795
 
3.1%
22 7051
12.0%
23 7726
13.2%
24 6395
10.9%
25 5067
8.6%
26 3874
6.6%
27 4450
7.6%
28 3707
6.3%
29 3270
5.6%
ValueCountFrequency (%)
123 1
 
< 0.1%
84 2
 
< 0.1%
80 2
 
< 0.1%
76 1
 
< 0.1%
73 3
 
< 0.1%
70 10
< 0.1%
69 6
< 0.1%
66 11
< 0.1%
65 13
< 0.1%
64 10
< 0.1%

person_income
Real number (ℝ)

Distinct2641
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64046.173
Minimum4200
Maximum1900000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size458.3 KiB
2025-03-11T14:53:52.886832image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum4200
5-th percentile28800
Q142000
median58000
Q375600
95-th percentile120000
Maximum1900000
Range1895800
Interquartile range (IQR)33600

Descriptive statistics

Standard deviation37931.107
Coefficient of variation (CV)0.59224627
Kurtosis342.62935
Mean64046.173
Median Absolute Deviation (MAD)17000
Skewness10.457723
Sum3.7559878 × 109
Variance1.4387689 × 109
MonotonicityNot monotonic
2025-03-11T14:53:53.024710image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60000 4164
 
7.1%
50000 2992
 
5.1%
30000 2200
 
3.8%
40000 1981
 
3.4%
70000 1876
 
3.2%
75000 1681
 
2.9%
45000 1604
 
2.7%
65000 1484
 
2.5%
80000 1472
 
2.5%
90000 1353
 
2.3%
Other values (2631) 37838
64.5%
ValueCountFrequency (%)
4200 1
 
< 0.1%
5000 1
 
< 0.1%
9600 14
 
< 0.1%
10000 1
 
< 0.1%
10140 1
 
< 0.1%
12000 37
0.1%
12360 1
 
< 0.1%
12500 1
 
< 0.1%
12600 1
 
< 0.1%
12996 2
 
< 0.1%
ValueCountFrequency (%)
1900000 1
 
< 0.1%
1839784 1
 
< 0.1%
1824000 1
 
< 0.1%
1200000 2
 
< 0.1%
948000 1
 
< 0.1%
928000 1
 
< 0.1%
900000 5
< 0.1%
889000 1
 
< 0.1%
828000 1
 
< 0.1%
780000 3
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size458.3 KiB
RENT
30594 
MORTGAGE
24824 
OWN
3138 
OTHER
 
89

Length

Max length8
Median length4
Mean length5.64118
Min length3

Characters and Unicode

Total characters330827
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRENT
2nd rowOWN
3rd rowOWN
4th rowRENT
5th rowRENT

Common Values

ValueCountFrequency (%)
RENT 30594
52.2%
MORTGAGE 24824
42.3%
OWN 3138
 
5.4%
OTHER 89
 
0.2%

Length

2025-03-11T14:53:53.157252image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-11T14:53:53.495419image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
rent 30594
52.2%
mortgage 24824
42.3%
own 3138
 
5.4%
other 89
 
0.2%

Most occurring characters

ValueCountFrequency (%)
R 55507
16.8%
E 55507
16.8%
T 55507
16.8%
G 49648
15.0%
N 33732
10.2%
O 28051
8.5%
M 24824
7.5%
A 24824
7.5%
W 3138
 
0.9%
H 89
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 330827
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 55507
16.8%
E 55507
16.8%
T 55507
16.8%
G 49648
15.0%
N 33732
10.2%
O 28051
8.5%
M 24824
7.5%
A 24824
7.5%
W 3138
 
0.9%
H 89
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 330827
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 55507
16.8%
E 55507
16.8%
T 55507
16.8%
G 49648
15.0%
N 33732
10.2%
O 28051
8.5%
M 24824
7.5%
A 24824
7.5%
W 3138
 
0.9%
H 89
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 330827
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 55507
16.8%
E 55507
16.8%
T 55507
16.8%
G 49648
15.0%
N 33732
10.2%
O 28051
8.5%
M 24824
7.5%
A 24824
7.5%
W 3138
 
0.9%
H 89
 
< 0.1%

person_emp_length
Real number (ℝ)

Zeros 

Distinct36
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7010146
Minimum0
Maximum123
Zeros7586
Zeros (%)12.9%
Negative0
Negative (%)0.0%
Memory size458.3 KiB
2025-03-11T14:53:53.593238image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q37
95-th percentile12
Maximum123
Range123
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.9597843
Coefficient of variation (CV)0.84232548
Kurtosis29.131606
Mean4.7010146
Median Absolute Deviation (MAD)2
Skewness2.0544297
Sum275691
Variance15.679892
MonotonicityNot monotonic
2025-03-11T14:53:53.704190image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 7586
12.9%
2 7270
12.4%
3 6475
11.0%
5 5839
10.0%
4 5461
9.3%
1 5203
8.9%
6 4902
8.4%
7 4275
7.3%
8 3015
 
5.1%
9 2290
 
3.9%
Other values (26) 6329
10.8%
ValueCountFrequency (%)
0 7586
12.9%
1 5203
8.9%
2 7270
12.4%
3 6475
11.0%
4 5461
9.3%
5 5839
10.0%
6 4902
8.4%
7 4275
7.3%
8 3015
 
5.1%
9 2290
 
3.9%
ValueCountFrequency (%)
123 2
 
< 0.1%
41 2
 
< 0.1%
39 1
 
< 0.1%
35 1
 
< 0.1%
31 5
< 0.1%
30 2
 
< 0.1%
29 2
 
< 0.1%
28 4
 
< 0.1%
27 7
< 0.1%
26 11
< 0.1%

loan_intent
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size458.3 KiB
EDUCATION
12271 
MEDICAL
10934 
PERSONAL
10016 
VENTURE
10011 
DEBTCONSOLIDATION
9133 

Length

Max length17
Median length15
Mean length10.003291
Min length7

Characters and Unicode

Total characters586643
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEDUCATION
2nd rowMEDICAL
3rd rowPERSONAL
4th rowVENTURE
5th rowMEDICAL

Common Values

ValueCountFrequency (%)
EDUCATION 12271
20.9%
MEDICAL 10934
18.6%
PERSONAL 10016
17.1%
VENTURE 10011
17.1%
DEBTCONSOLIDATION 9133
15.6%
HOMEIMPROVEMENT 6280
10.7%

Length

2025-03-11T14:53:53.812031image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-11T14:53:53.901836image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
education 12271
20.9%
medical 10934
18.6%
personal 10016
17.1%
venture 10011
17.1%
debtconsolidation 9133
15.6%
homeimprovement 6280
10.7%

Most occurring characters

ValueCountFrequency (%)
E 81216
13.8%
O 62246
10.6%
N 56844
9.7%
I 47751
8.1%
T 46828
8.0%
A 42354
 
7.2%
D 41471
 
7.1%
C 32338
 
5.5%
L 30083
 
5.1%
M 29774
 
5.1%
Other values (7) 115738
19.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 586643
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 81216
13.8%
O 62246
10.6%
N 56844
9.7%
I 47751
8.1%
T 46828
8.0%
A 42354
 
7.2%
D 41471
 
7.1%
C 32338
 
5.5%
L 30083
 
5.1%
M 29774
 
5.1%
Other values (7) 115738
19.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 586643
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 81216
13.8%
O 62246
10.6%
N 56844
9.7%
I 47751
8.1%
T 46828
8.0%
A 42354
 
7.2%
D 41471
 
7.1%
C 32338
 
5.5%
L 30083
 
5.1%
M 29774
 
5.1%
Other values (7) 115738
19.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 586643
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 81216
13.8%
O 62246
10.6%
N 56844
9.7%
I 47751
8.1%
T 46828
8.0%
A 42354
 
7.2%
D 41471
 
7.1%
C 32338
 
5.5%
L 30083
 
5.1%
M 29774
 
5.1%
Other values (7) 115738
19.7%

loan_grade
Categorical

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size458.3 KiB
A
20984 
B
20400 
C
11036 
D
5034 
E
 
1009
Other values (2)
 
182

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters58645
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowC
3rd rowA
4th rowB
5th rowA

Common Values

ValueCountFrequency (%)
A 20984
35.8%
B 20400
34.8%
C 11036
18.8%
D 5034
 
8.6%
E 1009
 
1.7%
F 149
 
0.3%
G 33
 
0.1%

Length

2025-03-11T14:53:54.008325image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-11T14:53:54.096255image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
a 20984
35.8%
b 20400
34.8%
c 11036
18.8%
d 5034
 
8.6%
e 1009
 
1.7%
f 149
 
0.3%
g 33
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 20984
35.8%
B 20400
34.8%
C 11036
18.8%
D 5034
 
8.6%
E 1009
 
1.7%
F 149
 
0.3%
G 33
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58645
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 20984
35.8%
B 20400
34.8%
C 11036
18.8%
D 5034
 
8.6%
E 1009
 
1.7%
F 149
 
0.3%
G 33
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58645
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 20984
35.8%
B 20400
34.8%
C 11036
18.8%
D 5034
 
8.6%
E 1009
 
1.7%
F 149
 
0.3%
G 33
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58645
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 20984
35.8%
B 20400
34.8%
C 11036
18.8%
D 5034
 
8.6%
E 1009
 
1.7%
F 149
 
0.3%
G 33
 
0.1%

loan_amnt
Real number (ℝ)

High correlation 

Distinct545
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9217.5565
Minimum500
Maximum35000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size458.3 KiB
2025-03-11T14:53:54.204695image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile2500
Q15000
median8000
Q312000
95-th percentile20000
Maximum35000
Range34500
Interquartile range (IQR)7000

Descriptive statistics

Standard deviation5563.8074
Coefficient of variation (CV)0.60360979
Kurtosis1.6937837
Mean9217.5565
Median Absolute Deviation (MAD)3000
Skewness1.1885785
Sum5.405636 × 108
Variance30955953
MonotonicityNot monotonic
2025-03-11T14:53:54.320348image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 7128
 
12.2%
5000 5176
 
8.8%
6000 4676
 
8.0%
12000 4311
 
7.4%
15000 3348
 
5.7%
8000 3296
 
5.6%
4000 2406
 
4.1%
3000 2304
 
3.9%
7000 2131
 
3.6%
20000 1777
 
3.0%
Other values (535) 22092
37.7%
ValueCountFrequency (%)
500 1
 
< 0.1%
700 1
 
< 0.1%
900 1
 
< 0.1%
1000 406
0.7%
1050 2
 
< 0.1%
1075 1
 
< 0.1%
1150 1
 
< 0.1%
1200 169
0.3%
1225 2
 
< 0.1%
1250 2
 
< 0.1%
ValueCountFrequency (%)
35000 155
0.3%
33000 1
 
< 0.1%
32000 1
 
< 0.1%
31000 2
 
< 0.1%
30750 1
 
< 0.1%
30000 111
0.2%
29800 2
 
< 0.1%
29100 1
 
< 0.1%
28250 1
 
< 0.1%
28000 55
 
0.1%

loan_int_rate
Real number (ℝ)

High correlation 

Distinct362
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.677874
Minimum5.42
Maximum23.22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size458.3 KiB
2025-03-11T14:53:54.435027image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum5.42
5-th percentile6.03
Q17.88
median10.75
Q312.99
95-th percentile15.7
Maximum23.22
Range17.8
Interquartile range (IQR)5.11

Descriptive statistics

Standard deviation3.0346972
Coefficient of variation (CV)0.28420424
Kurtosis-0.71815338
Mean10.677874
Median Absolute Deviation (MAD)2.74
Skewness0.20002032
Sum626203.95
Variance9.2093871
MonotonicityNot monotonic
2025-03-11T14:53:54.554650image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.99 2183
 
3.7%
7.51 2150
 
3.7%
7.88 1759
 
3.0%
7.49 1609
 
2.7%
13.49 1412
 
2.4%
11.49 1306
 
2.2%
7.9 1295
 
2.2%
5.42 1117
 
1.9%
6.03 1083
 
1.8%
11.71 1072
 
1.8%
Other values (352) 43659
74.4%
ValueCountFrequency (%)
5.42 1117
1.9%
5.43 1
 
< 0.1%
5.79 811
1.4%
5.99 562
1.0%
6 4
 
< 0.1%
6.03 1083
1.8%
6.05 1
 
< 0.1%
6.17 362
 
0.6%
6.39 104
 
0.2%
6.42 1
 
< 0.1%
ValueCountFrequency (%)
23.22 1
 
< 0.1%
23.06 1
 
< 0.1%
22.11 1
 
< 0.1%
22.06 1
 
< 0.1%
21.74 4
< 0.1%
21.64 1
 
< 0.1%
21.36 8
< 0.1%
21.21 5
< 0.1%
20.89 6
< 0.1%
20.86 2
 
< 0.1%

loan_percent_income
Real number (ℝ)

High correlation 

Distinct61
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15923751
Minimum0
Maximum0.83
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size458.3 KiB
2025-03-11T14:53:54.672339image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.04
Q10.09
median0.14
Q30.21
95-th percentile0.33
Maximum0.83
Range0.83
Interquartile range (IQR)0.12

Descriptive statistics

Standard deviation0.091691793
Coefficient of variation (CV)0.57581779
Kurtosis0.63134252
Mean0.15923751
Median Absolute Deviation (MAD)0.06
Skewness0.91747302
Sum9338.484
Variance0.0084073849
MonotonicityNot monotonic
2025-03-11T14:53:54.784248image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 3248
 
5.5%
0.13 3226
 
5.5%
0.08 2834
 
4.8%
0.17 2795
 
4.8%
0.07 2725
 
4.6%
0.11 2694
 
4.6%
0.09 2631
 
4.5%
0.12 2518
 
4.3%
0.14 2451
 
4.2%
0.06 2449
 
4.2%
Other values (51) 31074
53.0%
ValueCountFrequency (%)
0 2
 
< 0.1%
0.01 138
 
0.2%
0.02 423
 
0.7%
0.03 1129
 
1.9%
0.04 1689
2.9%
0.05 2137
3.6%
0.06 2449
4.2%
0.07 2725
4.6%
0.08 2834
4.8%
0.09 2631
4.5%
ValueCountFrequency (%)
0.83 1
 
< 0.1%
0.63 1
 
< 0.1%
0.59 1
 
< 0.1%
0.56 2
 
< 0.1%
0.55 1
 
< 0.1%
0.54 1
 
< 0.1%
0.53 4
 
< 0.1%
0.52 8
 
< 0.1%
0.51 20
 
< 0.1%
0.5 77
0.1%

cb_person_default_on_file
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.4 KiB
False
49943 
True
8702 
ValueCountFrequency (%)
False 49943
85.2%
True 8702
 
14.8%
2025-03-11T14:53:54.871227image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

cb_person_cred_hist_length
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8135561
Minimum2
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size458.3 KiB
2025-03-11T14:53:54.962541image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q13
median4
Q38
95-th percentile14
Maximum30
Range28
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.0291962
Coefficient of variation (CV)0.69306911
Kurtosis3.4907749
Mean5.8135561
Median Absolute Deviation (MAD)2
Skewness1.6185029
Sum340936
Variance16.234422
MonotonicityNot monotonic
2025-03-11T14:53:55.060382image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
3 10708
18.3%
2 10657
18.2%
4 10566
18.0%
9 3499
 
6.0%
8 3477
 
5.9%
7 3392
 
5.8%
6 3391
 
5.8%
10 3364
 
5.7%
5 3345
 
5.7%
14 927
 
1.6%
Other values (19) 5319
9.1%
ValueCountFrequency (%)
2 10657
18.2%
3 10708
18.3%
4 10566
18.0%
5 3345
 
5.7%
6 3391
 
5.8%
7 3392
 
5.8%
8 3477
 
5.9%
9 3499
 
6.0%
10 3364
 
5.7%
11 858
 
1.5%
ValueCountFrequency (%)
30 28
< 0.1%
29 26
< 0.1%
28 39
0.1%
27 46
0.1%
26 31
0.1%
25 31
0.1%
24 48
0.1%
23 35
0.1%
22 38
0.1%
21 37
0.1%

loan_status
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size458.3 KiB
0
50295 
1
8350 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters58645
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 50295
85.8%
1 8350
 
14.2%

Length

2025-03-11T14:53:55.158194image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-11T14:53:55.236423image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 50295
85.8%
1 8350
 
14.2%

Most occurring characters

ValueCountFrequency (%)
0 50295
85.8%
1 8350
 
14.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58645
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 50295
85.8%
1 8350
 
14.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58645
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 50295
85.8%
1 8350
 
14.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58645
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 50295
85.8%
1 8350
 
14.2%

Interactions

2025-03-11T14:53:51.249321image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:46.166315image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:46.913278image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:47.565350image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:48.379420image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:49.044855image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:49.731838image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:50.585037image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:51.331630image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:46.258099image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:46.993817image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:47.651942image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:48.462363image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:49.128994image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:49.821643image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:50.668476image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:51.411910image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:46.412103image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:47.070839image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:47.734740image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:48.541078image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:49.211471image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:50.020461image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:50.745285image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:51.498385image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:46.499324image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:47.156142image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:47.905792image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:48.629911image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:49.301283image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:50.134211image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:50.832277image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:51.579668image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:46.583045image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:47.239401image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:48.033208image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:48.710920image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:49.385928image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:50.235239image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:50.916663image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:51.664718image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:46.667944image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:47.324185image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:48.123239image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:48.798656image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:49.472653image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:50.328935image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:51.004889image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:51.749870image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:46.754825image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:47.409479image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:48.213427image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:48.885748image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:49.560610image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:50.420825image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:51.093977image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:51.828237image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:46.831793image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:47.485272image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:48.295352image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:48.964498image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:49.644344image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:50.502528image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T14:53:51.167408image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-03-11T14:53:55.297485image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
cb_person_cred_hist_lengthcb_person_default_on_fileidloan_amntloan_gradeloan_int_rateloan_intentloan_percent_incomeloan_statusperson_ageperson_emp_lengthperson_home_ownershipperson_income
cb_person_cred_hist_length1.0000.0060.0070.0460.014-0.0010.091-0.0290.0260.8050.0370.0430.102
cb_person_default_on_file0.0061.0000.0000.0490.6450.6060.0240.0430.1870.0080.0210.1000.006
id0.0070.0001.000-0.0030.004-0.0040.001-0.0040.0010.0060.0030.000-0.001
loan_amnt0.0460.049-0.0031.0000.0750.0830.0330.7170.1520.0620.0940.0680.366
loan_grade0.0140.6450.0040.0751.0000.7190.0270.0710.4610.0100.0250.1210.007
loan_int_rate-0.0010.606-0.0040.0830.7191.0000.0250.1440.410-0.001-0.1110.128-0.085
loan_intent0.0910.0240.0010.0330.0270.0251.0000.0170.1060.0650.0270.0920.000
loan_percent_income-0.0290.043-0.0040.7170.0710.1440.0171.0000.425-0.047-0.0620.094-0.327
loan_status0.0260.1870.0010.1520.4610.4100.1060.4251.0000.0190.0320.2420.026
person_age0.8050.0080.0060.0620.010-0.0010.065-0.0470.0191.0000.0650.0190.149
person_emp_length0.0370.0210.0030.0940.025-0.1110.027-0.0620.0320.0651.0000.0760.223
person_home_ownership0.0430.1000.0000.0680.1210.1280.0920.0940.2420.0190.0761.0000.029
person_income0.1020.006-0.0010.3660.007-0.0850.000-0.3270.0260.1490.2230.0291.000

Missing values

2025-03-11T14:53:51.947743image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-11T14:53:52.155974image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idperson_ageperson_incomeperson_home_ownershipperson_emp_lengthloan_intentloan_gradeloan_amntloan_int_rateloan_percent_incomecb_person_default_on_filecb_person_cred_hist_lengthloan_status
003735000RENT0.0EDUCATIONB600011.490.17N140
112256000OWN6.0MEDICALC400013.350.07N20
222928800OWN8.0PERSONALA60008.900.21N100
333070000RENT14.0VENTUREB1200011.110.17N50
442260000RENT2.0MEDICALA60006.920.10N30
552745000RENT2.0VENTUREA90008.940.20N50
662545000MORTGAGE9.0EDUCATIONA120006.540.27N30
772120000RENT0.0PERSONALC250013.490.13Y30
883769600RENT11.0EDUCATIOND500014.840.07Y110
9935110000MORTGAGE0.0DEBTCONSOLIDATIONC1500012.980.14Y60
idperson_ageperson_incomeperson_home_ownershipperson_emp_lengthloan_intentloan_gradeloan_amntloan_int_rateloan_percent_incomecb_person_default_on_filecb_person_cred_hist_lengthloan_status
58635586353269000RENT0.0DEBTCONSOLIDATIONB1200010.200.17N71
58636586362437000RENT3.0EDUCATIONC900013.490.24Y20
58637586372475000RENT8.0VENTUREB400010.750.05N40
58638586382946610MORTGAGE1.0PERSONALD260017.580.05N61
58639586392270000RENT6.0DEBTCONSOLIDATIONA100007.290.14N40
586405864034120000MORTGAGE5.0EDUCATIOND2500015.950.21Y100
58641586412828800RENT0.0MEDICALC1000012.730.35N81
58642586422344000RENT7.0EDUCATIOND680016.000.15N21
58643586432230000RENT2.0EDUCATIONA50008.900.17N30
58644586443175000MORTGAGE2.0VENTUREB1500011.110.20N50